Testing the performance of a 2D nearest point algorithm with genetic algorithm generated Gaussian distributions

Authors:
Janne Koljonen;Markus Mannila;Merja Wanne
Affiliations:
Department of Electrical Engineering and Automation, University of Vaasa, P.O. Box 700, FIN-65101 Vaasa, Finland;Department of Computer Science, University of Vaasa, P.O. Box 700, FIN-65101 Vaasa, Finland;Department of Computer Science, University of Vaasa, P.O. Box 700, FIN-65101 Vaasa, Finland
Venue:
Expert Systems with Applications: An International Journal
Year:
2007

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Abstract

Genetic algorithms have successfully been used in automatic software testing. Particularly programming errors and inputs that conflict with time constraints can be found. In this paper, the idea of genetic algorithm based software testing is broadened to algorithm performance testing. It is shown how the best and worst case performance of the algorithms can be found effectively. This information can be further utilized when comparing and improving algorithms. In this paper, the proposed test method is introduced and the advantages of using genetic algorithms are discussed. Furthermore, the proposed method is applied to a 2D nearest point algorithm, which is tested by optimizing the parameters of 2D Gaussian distributions using genetic algorithms in order to find the best and worst case distributions and the corresponding performances.